Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
A connectionist model for selection of cases
Information Sciences: an International Journal
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Instance Selection and Construction for Data Mining
Instance Selection and Construction for Data Mining
Machine Learning
Exploiting unlabeled data in ensemble methods
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning subjective nouns using extraction pattern bootstrapping
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Rapid and brief communication: Active learning for image retrieval with Co-SVM
Pattern Recognition
An active feedback framework for image retrieval
Pattern Recognition Letters
Locality sensitive semi-supervised feature selection
Neurocomputing
A unified framework for semi-supervised dimensionality reduction
Pattern Recognition
A lazy bagging approach to classification
Pattern Recognition
An association-based case reduction technique for case-based reasoning
Information Sciences: an International Journal
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
Personalized mode transductive spanning SVM classification tree
Information Sciences: an International Journal
Information Sciences: an International Journal
K Nearest Neighbor Equality: Giving equal chance to all existing classes
Information Sciences: an International Journal
An improved fast edit approach for two-string approximated mean computation applied to OCR
Pattern Recognition Letters
On the use of meta-learning for instance selection: An architecture and an experimental study
Information Sciences: an International Journal
Hi-index | 0.07 |
This paper proposes a novel method for nearest neighbor editing. Nearest neighbor editing aims to increase the classifier's generalization ability by removing noisy instances from the training set. Traditionally nearest neighbor editing edits (removes/retains) each instance by the voting of the instances in the training set (labeled instances). However, motivated by semi-supervised learning, we propose a novel editing methodology which edits each training instance by the voting of all the available instances (both labeled and unlabeled instances). We expect that the editing performance could be boosted by appropriately using unlabeled data. Our idea relies on the fact that in many applications, in addition to the training instances, many unlabeled instances are also available since they do not need human annotation effort. Three popular data editing methods, including edited nearest neighbor, repeated edited nearest neighbor and All k-NN are adopted to verify our idea. They are tested on a set of UCI data sets. Experimental results indicate that all the three editing methods can achieve improved performance with the aid of unlabeled data. Moreover, the improvement is more remarkable when the ratio of training data to unlabeled data is small.